| name | bioconductor-biobase |
| description | Functions that are needed by many other packages or which replace R functions. |
| when_to_use | Use when: Coordinating high-throughput genomic data (e.g., microarray expression values) and phenotype metadata into a single, structured ExpressionSet object.; Evaluating statistical functions across sample strata defined by covariates using esApply.; Updating older, serialized instances of Bioconductor objects to their current class representations using updateObject().. Not for: For modern single-cell or range-based sequencing data, use SummarizedExperiment or SingleCellExperiment instead because they natively support genomic coordinates and scale better to sparse, multi-assay datasets.; For purely tabular data manipulation, |
| user-invocable | false |
Biobase
Functions that are needed by many other packages or which replace R functions.
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
- Version: 2.72.0 · Bioconductor: 3.23 · R: ≥ 4.6
- Depends: BiocGenerics
- Install:
BiocManager::install("Biobase")
When to Use
- Coordinating high-throughput genomic data (e.g., microarray expression values) and phenotype metadata into a single, structured
ExpressionSet object.
- Evaluating statistical functions across sample strata defined by covariates using
esApply.
- Updating older, serialized instances of Bioconductor objects to their current class representations using
updateObject().
When NOT to Use
- For modern single-cell or range-based sequencing data, use
SummarizedExperiment or SingleCellExperiment instead because they natively support genomic coordinates and scale better to sparse, multi-assay datasets.
- For purely tabular data manipulation, use
dplyr or data.table instead because eSet objects enforce strict matrix dimensions and metadata alignment that complicate simple tidy-data workflows.
Data Requirements
- High-throughput data matrices (e.g., expression values) where rows represent features and columns represent samples.
- Sample covariates formatted as an
AnnotatedDataFrame.
- Feature covariates formatted as an
AnnotatedDataFrame.
- Normalization state: Typically normalized quantitative data (e.g., log-expression), though raw matrices can be stored.
Key Parameters
- assayData (assayDataNew()): High-throughput data stored as a list, environment, or lockedEnvironment containing identically sized matrices.
- phenoData (AnnotatedDataFrame()): Sample covariates matching the column names of
assayData.
- featureData (AnnotatedDataFrame()): Feature covariates matching the row names of
assayData.
- experimentData (MIAME()): Experimental description and metadata.
- annotation (character()): Label identifying the associated annotation package.
Best Practices
- Use accessor functions like
exprs() and pData() to retrieve or assign data rather than accessing slots directly.
- Ensure that all matrices within
assayData have identical row and column dimensions, and that featureNames and sampleNames match across all slots.
- Call
validObject() after making structural modifications to an eSet to guarantee that the object remains internally consistent.
- Use
storageMode() to check or set how assayData is stored, preferring lockedEnvironment to prevent accidental side-effects while maintaining memory efficiency.
Common Pitfalls
- Modifying a
lockedEnvironment directly causes an error; fix this by extracting the element, modifying it, and reassigning it, or using replacement methods like exprs(obj) <- value.
- Variable shadowing occurs when applying functions with
esApply; fix this by ensuring the applied function correctly references the covariate names populated from the pData dataframe.
- Objects become transiently invalid during manual slot modification; fix this by calling
validObject() after updates to ensure dimensional consistency across assayData and phenoData.
Alternatives
SummarizedExperiment: The modern Bioconductor standard for containerizing genomic data, supporting coordinate-based genomic ranges.oligo: Provides alternative, specialized class implementations for handling SNP and exon array data.MultiAssayExperiment: For integrating multiple different omics assays on the same set of biological specimens.
Citations
- R. Gentleman, V. Carey, M. Morgan, S. Falcon and H. Khan. "esApply Introduction".
- Martin T. Morgan and H. Khan. "Biobase development and the new eSet".
References
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